ABSTRACT

Malaria remains a major public health problem, causing 435,000 deaths in 2017. The objective of this study was to estimate the prediction ability of vector species associated with the prediction power of environmental and socio-economic factors for malaria risk. Logistic regression was used for malaria risk estimation. A Radial Basis Function model was applied for estimating the predictive ability of Anopheles species, environmental and socio-economic factors. The lowest fever prevalence was found where Anopheles melas was dominant. Anopheles coluzzi and Anopheles gambiae were the dominant species where prevalence of malaria was high. Altitude, country and vector species were the best predictive factors. Anopheles arabiensis, An. coluzzi and An. gambiae were most common in urban areas. This study will improve the prediction of malaria risk in targeted areas. We have observed how important it is to adapt health policies according to the dominant malaria vector in a region.

摘要

疟疾仍然是一个主要的公共卫生问题，在 2017 年造成 435,000 人死亡。本研究的目的是估计与环境和社会经济因素对疟疾风险的预测能力相关的病媒物种的预测能力。逻辑回归用于疟疾风险估计。应用径向基函数模型来估计 按蚊 物种、环境和社会经济因素的预测能力。在黑按蚊 占主导地位 的地方发现了最低的发烧流行率 。Anopheles coluzzi 和 Anopheles gambiae 是疟疾流行率高的优势物种。海拔、国家和媒介物种是最好的预测因素。 阿拉伯按蚊，An。科鲁齐 和 安。冈比亚 在城市地区最为常见。这项研究将改善对目标地区疟疾风险的预测。我们已经观察到根据一个地区的主要疟疾病媒调整卫生政策是多么重要。